Navigating the New Frontier: Cybersecurity & Privacy Risks of Generative AI

The Double-Edged Sword of Generative AI

Generative AI brings groundbreaking capabilities—but with power comes risk. From deepfakes to insider threats, businesses now face a complex, evolving threat landscape. For organizations embracing AI, security-first design and proactive governance are essential.

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The Rising Cybersecurity Threats in AI

– Hidden Malware via AI Images

• Recent research highlights a stealthy new attack vector: hidden malware embedded in images processed by AI chatbots. For instance, when platforms downscale images (e.g. via bicubic interpolation), it can reveal malicious prompts that trigger unintended actions—like siphoning data from users’ tools or calendars without their knowledge. Standard firewalls are often blind to these threats, making layered defenses and user-level input checks critical.

– Exploitation via AI-Powered Insider Threats

• Generative AI isn’t just a tool for attackers—it can empower insider threats. A survey by Exabeam reveals that over 64% of organizations now see insiders (malicious, negligent, or AI-compromised) as a greater cybersecurity risk than external actors. AI agents can mimic legitimate users, moving quickly and stealthily under authorized credentials. Yet less than half of organizations use behavior analytics to detect these threats.

– Prompt Injection: The Vulnerability in LLM Inputs

• Prompt injection has emerged as one of the top risks in LLM security—ranking high in OWASP’s 2025 “Top 10 for LLM Applications.” Attackers embed malicious instructions in user prompts or hidden within image or text inputs, forcing models to execute unintended behavior. Even external documents or websites used in retrieval-augmented scenarios can trigger model manipulation. Current mitigations—like prompt filtering, human oversight, or data hygiene—help, but no surefire solution exists yet.

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Broader Privacy & Data Leakage Risks

– Unintentional Memorization & Exposure

• Generative models trained on vast datasets can unintentionally memorize and regurgitate sensitive data, increasing the risk of privacy breaches. Proprietary or user-submitted data is especially vulnerable when models are fine-tuned without proper safeguards.

– Ethical Worries Over Data Handling

• A Deloitte study found that nearly 75% of IT and business professionals cite data privacy as a top-three ethical concern with enterprise AI adoption. This reflects widespread unease over how AI systems handle and potentially misuse data.

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How One Ring can help

– Proactive Risk Management & AI-Safety Frameworks

• Threat modeling to identify risks like hidden-malware, prompt injection, and insider misuse.

• Governance & oversight structures ensuring AI input/output hygiene and human-in-the-loop controls.

• Privacy-first model training via anonymization, data minimization, and secure infrastructure (e.g., confidential computing)

– Detection & Behavioral Analytics

• Deploy user and entity behavior monitoring to detect anomalous activities—especially AI-driven insider threats.

• Implement prompt-filtering tools and adversarial testing (e.g., injection-resistant templates, RAG input vetting).

– Awareness, Training & Governance

• Train staff on prompt injection risks and safe prompt practices.

• Offer policies and documentation aligning AI use with standards like NIST’s updated frameworks.

 

“It does not do to leave a live dragon out of your calculations, if you live near him.” — J.R.R. Tolkien

 

Connect with us for more information about how we can help!

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Sources:

“AI chatbot users beware – hackers are now hiding malware in the images served up by LLMs”, TechRadar Pro, URL: techradar.com/pro/security/ai-chatbot-users-beware-hackers-are-now-hiding-malware-in-the-images-served-up-by-llms

“AI set to supercharge insider threats – as cybersecurity professionals warn of an impending AI agent onslaught”, TechRadar Pro, URL: techradar.com/pro/security/ai-set-to-supercharge-insiders-threat-as-cybersecurity-professionals-warn-of-an-impending-ai-agent-onslaught

“Generative AI Is Changing Data Privacy Expectations”, TrustArc, URL: trustarc.com/resource/generative-ai-changing-data-privacy-expectations

“Ethical technology standards: Today’s organizational practices”, Deloitte, URL: deloitte.com/us/en/about/governance/technology-trust-ethics-annual-report.html

“2025 Top 10 Risk & Mitigations for LLMs and GenAI Apps”, OWASP, URL: genai.owasp.org/llm-top-10/